March 03, 2022
Achieving Effective Data Analytics
Organizations that treat data as a valuable asset give themselves an advantage over competitors.
IN THIS ARTICLE
Data Analytics Is Changing the Game
Data Analytics for SMBs
Enabling Effective Data Analytics
Strategies for Data Analytics Success
Realizing Business Outcomes Through Data Analytics
The notion that every company is a data company is so widely accepted it’s almost a cliché. Still, many organizations continue to struggle even to inventory and organize their data, much less leverage it in a way that helps them arrive at transformative insights.
Data analytics can help organizations improve customer service, increase profits, enhance security, predict industry trends and achieve other benefits — but only with the right mix of strategies, tools, processes and practices. Effective analytics programs require organizations to implement processes such as data cataloging and governance and to embrace culture changes and build internal expertise. Additionally, organizations must implement tools that provide capabilities around storage, transformation, discovery and analysis.
As their data analytics programs mature, organizations should constantly seek out new opportunities to use data in innovative ways that create value and provide a competitive edge. This requires the adoption of a comprehensive data management strategy. Often, organizations that lack internal analytics expertise rely on a trusted partner such as CDW to help develop this strategy and put it into action.
CDW has focused on tailoring solutions to data discovery, storage, analysis and other functions to help ease organizations’ paths to effective data analytics and set up teams for success. Many organizations look to the public cloud for data analytics capabilities, giving themselves increased flexibility.
THE CLOUD AND DATA ANALYTICS
Many organizations are moving their data to the cloud — and moving their analytics operations along with it.
A general rule of thumb is that organizations should run their analytics close to where their data is, to optimize both cost and performance.
Public cloud hyperscalers each have their own platforms with their own strengths, and each offers much greater flexibility than a strictly on-premises environment.
Many organizations already house their data in the public cloud, and the cloud is a natural fit for their analytics as well.
Data Analytics Is Changing the Game
It is no secret that effective data analytics can create massive value for enterprises across industries. Organizations must adopt a strategic approach to data analytics, and while each organization’s path to effective data analytics will be unique, analytics environments tend to follow a common, five-stage progression toward maturity.
While most analytics programs will begin with manual reporting, there’s a reason why organizations at this stage should look for opportunities to level up their analytics. Manual reporting often leads to increased errors and costs, as well as poor productivity and difficulty managing workflows.
INSIGHTS AND MODELING
Many times, the initial applications of data analytics programs are focused on IT — partly because of the ready wealth of data produced by IT environments. However, after organizations have applied a strategic framework to their analytics efforts, more opportunities to use data in innovative ways become apparent.
Business intelligence encompasses not only data visualization but also the collection, integration and analysis of raw data, often using dedicated tools. We see BI tools often being used by individual business units throughout an organization — from finance departments to sales managers and marketing teams — to track key metrics.
For most organizations, the end goal of data initiatives will be predictive and prescriptive analytics, which answers the questions of what is likely to happen and what is needed for improvement. Use cases can help forecast customer demand, optimize sales strategies, improve asset lifecycle management, manage risks and fine-tune operations.
METHODOLOGY AND STRATEGY
To use data in ways that truly begin to transform the organization, leaders must take a strategic approach to data and analytics and put in place methodologies designed to maximize the value of data within the enterprise. Stakeholders should take a close look at their governance policies and storage practices.
Want to learn how CDW can help put your data to innovative uses with a modern data analytics strategy?
Acquiring Data Analytics Talent
Organizations should seek to maximize their access to analytics talent and should encourage their data professionals to look for new ways to derive value from data.
Business leaders should take a selective approach to hiring, assessing when specialists are truly needed and when analytics objectives can be accomplished through services or automated tools.
Organizations can leverage their current employees by training them in machine learning, data analytics, data modeling, data architecture and data engineering.
Universities, business incubators and startups may provide pathways to partnerships that bring data analytics talent into larger organizations.
Data Analytics for SMBs
Small and midsize businesses (SMBs) lack the robust IT budgets of large corporations, and few smaller organizations can afford advanced data analysis tools or a team of data scientists. That doesn’t mean they can’t benefit from analytics applications.
By 2025, IDC predicts, 30 percent of SMBs will adopt cloud-native, data-driven apps that intelligently adapt their behavior to the needs of the business. “SMBs are apt to follow an app’s default design, likely because they do not have the IT resources for customizations,” IDC notes. “The future of data-driven apps … offers SMBs an unprecedented opportunity to automate and streamline their data/content management and gain insight for better planning and business process orchestration.”
IDC gives the example of a patient onboarding app for small healthcare organizations. Typically, doctors run through all the questions (even irrelevant ones) with new patients simply to get to the next screen in the app. But data-driven apps will, IDC notes, eventually omit data fields that aren’t necessary.
“Over time,” IDC notes, “the confidence level of the data recommendations will improve and move humans to more of a governance role.”
Enabling Effective Data Analytics
An effective data analytics strategy must account for common challenges that can be overcome with key data capabilities.
A data catalog is an inventory of data assets within an organization, which can help data scientists and other stakeholders to collect, organize, access and enrich metadata that assists with overall data management. Without a data catalog, many business and IT leaders would struggle to identify the sources of data within their organizations, let alone where all that data resides.
Advanced storage solutions provide a solid foundation for data analytics initiatives. These include next-generation databases, data warehouses and data lakes. Next-generation databases are specifically built for speed and scale, while a data lake is a large pool of raw data. A data warehouse, by contrast, is a repository for structured, filtered data that has already been processed for specific purposes.
Data security should be a key component of any IT initiative, especially one centered on Big Data (where a breach could potentially expose massive amounts of information to cybercriminals). In fact, some observers, have dubbed highly sensitive information “toxic data,” due to its ability to harm an organization if it falls into the wrong hands.
Organizations must transform their stored data into a more easily accessible asset. Data cleansing solutions standardize data formats, and data merge solutions combine data from multiple sources. Master data management (MDM) tools allow business and IT leaders to create a single source of truth across their organizations. Without MDM tools in place, many organizations struggle to answer relatively basic questions.
Data governance encompasses the policies, processes and organizational structures that support data management and analytics. Organizations face governance challenges including where and how long to store data, who can access which data (and how they can access it), how data is shared across the organization and how to cull redundant records that contain identical information.
Data discovery tools, such as data catalogs, help organizations to arrive at a better and more complete understanding of what data assets exist in their environments. Only after engaging in data discovery can business and IT leaders begin to make an informed assessment about what sorts of objectives can be achieved using the information resources they already have.
As they adopt new data analytics tools and processes, organizations must shift their cultures in a way that embraces both employee experiences and data-driven insights. For instance, leaders should be careful not to discount the experience of sales team members, who have intimate knowledge of customers’ needs, and instead enhance that firsthand knowledge with insights derived from data analytics tools.
To power data analysis, organizations must invest in solutions such as data visualization tools and IT operational analytics (ITOA). ITOA can assist with functions such as root cause analysis, and it is frequently an early use case for organizations investing in data analytics for the first time.
Interested in learning how to ensure the effectiveness of your data analytics strategy to give your organization a competitive advantage?
The Future of Data Analytics
A 2021 Gartner report makes predictions about where data analytics is headed in the coming years. By 2022, the report says, over 75 percent of centrally organized analytics programs will be replaced by a hybrid organizational model that shares power with local domain data and analytics leaders. In addition, the report lists these upcoming trends for data analytics.
The percentage of organizations that will require a professional code of conduct incorporating ethical use of data and AI by 2023
The percentage of organizations that will combine components from three or more analytics solutions to build applications by 2023
The percentage of the primary data responsibilities that will be related to data created, managed and analyzed in edge environments by 2023
The percentage of organizations that will shift their focus to “small” and “wide” data analytics by 2025
Source: gartner.com, "Top Trends in Data and Analytics for 2021," Feb. 16, 2021.
Strategies for Data Analytics Success
Some IT leaders may get swept up in the technologies powering data analytics. Instead, stakeholders should first focus on outcomes and applications, then seek out solutions that will help them achieve their goals.
To maintain this emphasis on use cases, CDW recommends that organizations ensure that their data analytics initiatives are guided by an overarching strategy. These strategies will vary from organization to organization, but nearly all analytics efforts will be propped up, at least in part, by the pillars of data management, artificial intelligence (AI) and machine learning (ML), and security.
A data management strategy is essential to analytics success. It maps an organization’s use of data to its goals, ensuring that the disparate activities surrounding data management — from collection to collaboration — work together effectively, efficiently and seamlessly. Without a data management strategy in place, organizations often run into problems including incompatible, duplicative or missing data. They may find themselves running siloed projects that use common data yet rely on redundant hours and costs. To create a strong data management strategy, organizational and IT leaders must identify their business objectives, create effective processes and find the technologies that meet the needs of their use cases.
The use of AI and ML tools in data analytics programs makes it possible for organizations to obtain insights about their customers and constituents, expand their business, and optimize the quality and speed of logistics. Business and IT leaders should be willing to experiment when implementing AI and ML in their analytics efforts, and should quickly move past any unsuccessful projects, maintaining a “think big, start small, fail fast” mindset. By beginning with specific business outcomes that can easily be measured and understood, stakeholders will be able to accurately assess how well their AI and ML projects are working and use these lessons to make improvements.
Data analytics can be a powerful weapon in an organization’s security efforts — but organizations must take great care to safeguard their data storage and analytics infrastructure to prevent a potentially catastrophic breach. Security analytics tools can help stakeholders to identify changing use patterns, execute rapid analysis in real time and perform complex correlations across a variety of data sources. These data sources may range from server and application logs to network events and user activities. These security efforts require advanced analytics as well as the ability to run analysis on large amounts of current and historical data. However, by integrating security into their analytics programs, organizations can improve their cyber resilience, limit exposure to malware and protect their reputations.
Jim Vanden Boom